The Solutions Review editors are exploring how and why companies should develop a marketing strategy that prioritizes Generative Engine Optimization (GEO) over traditional SEO best practices.

The ongoing, but probably irreversible, shift from traditional search engines to generative AI platforms represents one of the most significant disruptions to digital marketing since Google’s PageRank algorithm fundamentally restructured how information gets discovered online. While SEO practitioners spent decades optimizing for ten blue links, the emergence of ChatGPT, Perplexity, Claude, and similar platforms has created an entirely new paradigm where AI systems are synthesizing information and presenting direct answers rather than offering pathways to websites. This isn’t merely an evolution of search marketing—it’s a complete reimagining of how brands must position themselves in the information ecosystem.

With that in mind, the Solutions Review editors are exploring how enterprise technology brands can pivot their search engine optimization (SEO) strategy into a generative engine optimization (GEO) strategy, and why doing so is no longer a question of whether to develop one, but rather how quickly it can be launched.

The Fundamental Difference Between SEO and GEO

Search engine optimization operates on a relatively straightforward premise: convince algorithmic crawlers that your content deserves prominent placement in results pages, then capture clicks from users who have signaled their intent through query formulation. The entire framework assumes that users will navigate to your domain, consume your content on your terms, and potentially convert within your controlled environment.

However, generative engine optimization functions on entirely different mechanics. AI platforms don’t drive traffic to your website, but consume your content as training data or reference material, then reconstitute that information within their own response frameworks. The result is that users never have to leave the AI interface, since it provides them with synthesized answers that may draw from dozens of sources simultaneously, with attribution ranging from explicit citations to complete opacity depending on the platform and query type. While these AI platforms are still learning and the methods they use to learn are evolving (in response to legal and ethical developments, among others), the results are clear: people want them to stick around.

This transformation means that traditional conversion funnels could be on the edge of total collapse. The moment of engagement isn’t when someone clicks through to your site but when an AI model incorporates your perspective, data, or framework into its response. Your marketing success depends not on capturing attention through search result placement but on becoming an authoritative source that AI systems reliably reference when addressing queries in your domain.

Understanding How Generative Engines Process Information

Most current generative AI platforms operate through a combination of training data and retrieval-augmented generation (RAG). The training data represents a snapshot of internet content up to a specific cutoff date. Meanwhile, RAG systems enable models to access and incorporate more recent information through web searches or real-time document retrieval. This architecture creates several optimization opportunities that differ fundamentally from traditional SEO.

For one, training data integration means that high-quality, authoritative content published before the model’s cutoff date becomes baked into the model’s understanding of topics. The AI doesn’t need to retrieve this information because it already understands concepts through the patterns it learned during training. Consequently, content that influences training data shapes how models perceive entire topic areas.

RAG systems present different dynamics. When an AI platform performs retrieval to answer queries, it evaluates sources based on relevance, recency, and authority, then synthesizes that information from multiple retrieved documents. The goal isn’t to rank first in a traditional sense but to be included in the retrieval set and to provide information structured in ways that models can easily extract and integrate into coherent responses.

As you can imagine, platform-specific approaches vary significantly. Some AI systems provide explicit citations with links, creating a new form of referral traffic. Others synthesize information without attribution, making brand recognition and repeated exposure across multiple sources the only viable strategy for establishing mind share. Still others allow users to access source documents directly, transforming the AI interface into a discovery layer rather than a final destination.

Strategic Pillars for Generative Engine Optimization

Understanding how generative engines work is one thing, but knowing how to optimize your brand’s content and identity for them is another. That’s where the term Generative Engine Optimization (GEO) comes from. As new a strategy as it is, it’s already gaining momentum and proving to be a powerful addition to marketing strategies across markets.

Authority Architecture

Traditional SEO prioritizes domain authority as a holistic metric aggregating backlinks, traffic, and trust signals across an entire website. GEO, meanwhile, requires a more granular approach to authority that focuses on topical expertise and source credibility within specific knowledge domains. Additionally, counter to traditional SEO wisdom, which encourages broad keyword targeting, GEO often rewards extreme specialization. Being the unambiguous authority on a narrow topic makes you indispensable for AI systems addressing that subject, while being one of thousands of reasonable sources on a broad topic makes you easily substitutable in synthetic responses.

Building authority for AI systems means establishing your organization as the definitive source for specific concepts, frameworks, datasets, or methodologies. This requires moving beyond keyword-focused content toward creating comprehensive resources that demonstrate genuine expertise. AI models trained on authoritative sources internalize not just individual facts but entire conceptual frameworks, making depth and interconnection more valuable than breadth.

Verification mechanisms also matter more in generative contexts than in traditional search. AI platforms are increasingly incorporating source quality assessments into their retrieval and synthesis processes. As a result, organizations that can demonstrate expertise through credentials, peer review, institutional backing, or verifiable track records gain disproportionate influence in how models represent information in their respective domains.

Structured Knowledge Representation

Generative AI models excel at extracting structured information from unstructured text, but explicitly structured content makes this process exponentially more reliable. Organizations that format knowledge in ways that align with how AI systems process information gain significant advantages in retrieval and synthesis accuracy. Schema markup, which provided marginal benefits in traditional SEO, becomes far more valuable for GEO. Structured data enables AI systems to understand the relationships between entities, extract specific data points, and maintain accuracy when synthesizing information across multiple sources.

Semantic clarity in content structure helps models parse meaning correctly. This means moving away from creative headline writing and metaphorical language toward explicit, unambiguous expression of concepts. While traditional SEO sometimes rewards clever wordplay that captures long-tail searches, GEO favors straightforward articulation that models can confidently interpret and replicate.

Documentation formats that separate context from core information improve extractability. When AI systems retrieve content, they need to distinguish between background explanations and actionable insights, between qualifications and central claims, and between your perspective and the consensus. Content that makes these distinctions explicit through clear structural elements gets represented more accurately in AI responses.

Temporal Optimization

The time dimension functions differently in generative engine optimization compared to traditional search. SEO frequently emphasizes freshness signals, rewarding recently published or updated content with temporary ranking boosts. GEO creates a bifurcated temporal landscape where both historical influence and real-time relevance matter, but through separate mechanisms.

Content that influences training data carries significant weight in how models understand fundamental concepts, creating a strong incentive to publish authoritative frameworks and original research as early as possible to claim conceptual territory before competitors. Simultaneously, RAG systems create demand for continuously updated information on evolving topics. Organizations that maintain current and accurate data on developing situations position themselves as essential sources for real-time analysis and synthesis.

One potential outcome of this new approach is a divergence in content strategies, with one avenue focusing on foundational content that aims to shape AI training data, and the other on dynamic content intended for retrieval systems. Organizations that recognize this split and allocate resources to both types of content appropriately will outperform those applying uniform approaches across all material.

Tactical Implementation Approaches

Translating GEO principles into operational reality requires moving beyond conceptual frameworks into concrete content development and technical optimization practices. The strategic pillars outlined above provide directional guidance, but execution demands specific techniques for structuring information, formatting content, and establishing entity relationships that generative AI systems can reliably process and incorporate. The tactics that follow represent new approaches to content creation that prioritize machine extractability alongside human readability, recognizing that your primary audience now includes AI systems that will mediate how humans ultimately encounter your ideas.

Entity Optimization

Generative AI models understand information through entities and their relationships rather than through keywords and phrases. Optimizing for entity recognition means ensuring that your organization, products, executives, methodologies, and other important entities get consistently and accurately represented in AI knowledge bases. As such, variations in how you refer to your organization, products, or concepts can create ambiguity that models may resolve incorrectly, potentially conflating your entities with those of competitors or fragmenting understanding across multiple representations.

Meanwhile, entity-relationship articulation clarifies how different concepts are connected. Explicitly stating relationships between your organization and industry standards, between your products and use cases, and between your executives and their areas of expertise helps models build accurate knowledge graphs that inform how they discuss your entities in synthetic responses.

Multi-Modal Content Strategy

Text-focused optimization makes sense when targeting search engines that primarily process written content. Generative AI platforms are increasingly incorporating multimodal capabilities, which enable them to understand and generate images, analyze videos, and process other content formats beyond pure text. For example, AI systems can now process video transcripts and, increasingly, visual content from video frames. This means that organizations producing video content should ensure comprehensive transcription, time-stamped descriptions of visual elements, and structured metadata that help models understand not just what’s said but also what’s shown.

Visual content optimized for AI interpretation understandably requires different approaches than visual content designed for human consumption. Alt text, captions, and the surrounding textual context help models understand images, but layout, diagram structure, and the visual information hierarchy also play a role. Charts, infographics, and data visualizations that clearly label axes, include legends, and maintain high contrast support accurate AI interpretation.

Multi-modal content, specifically video, will likely become significantly more valuable for GEO as models improve at cross-modal reasoning. Organizations that invest in creating rich media content with strong structural signals and comprehensive metadata will establish a competitive advantage as AI platforms incorporate these capabilities into their retrieval and synthesis workflows.

Measurement and Analytics for GEO

Traditional SEO metrics focus on rankings, traffic, and conversions within your owned properties. GEO is something altogether new, though, and requires developing new measurement frameworks that account for influence and presence within AI platforms that don’t drive traffic to your site.

One way to track your company’s GEO efforts is through citation tracking across AI responses, which provides a direct measurement of when platforms reference your content. Organizations serious about GEO need to develop monitoring capabilities that track both explicit citations and implicit incorporation of your ideas and frameworks. Some tools have emerged to monitor these citations, although the landscape remains immature compared to traditional SEO analytics. Teams should keep a close eye on that market as it develops.

Brand mention analysis in AI responses measures when platforms discuss your organization, products, or executives without necessarily citing specific content. This softer metric captures presence in the AI information ecosystem even when direct attribution doesn’t occur. Another approach is to track competitive positioning in AI responses, which reveals how models present your organization relative to competitors. When users ask comparative questions or request recommendations, understanding how AI platforms position your offerings relative to alternatives provides crucial strategic intelligence.

The challenge is that generative AI platforms provide limited visibility into when and how they use content, making measurement inherently more difficult than traditional analytics. Organizations should expect to invest significantly in developing proprietary measurement approaches until the analytics ecosystem matures.

Looking Forward: The Evolution of Generative Engine Optimization

The current state of GEO represents an early-stage territory, as organizations figure out how to influence AI systems that are themselves rapidly evolving. Several developments are likely to dramatically reshape the field over the next few years, so brands must ensure their marketing and GEO-centric strategies can adapt to whatever new trends emerge. Here are a few predictions for what trends could be on the horizon:

• Retrieval mechanisms will become more sophisticated, incorporating quality signals that reward authoritative sources and penalize low-quality content farms attempting to game generative systems.
• Personalization in AI responses will fragment the optimization landscape. As models learn individual user preferences and tailor responses accordingly, universal optimization strategies become less effective. Organizations will need to consider how to remain relevant across diverse, personalized contexts rather than optimizing for a single, canonical response.
• Commercial integration of AI platforms will create new paid placement opportunities alongside organic optimization. Early signals suggest that sponsored content within AI responses may evolve into a significant revenue stream for platform operators, although business models remain uncertain.

Success in the generative AI era requires accepting that you may never control the environment where your ideas get consumed. Instead, you can influence the knowledge ecosystem that AI systems draw from, shaping how they think about and present information in your domain. Organizations that embrace this shift and develop robust GEO capabilities now will establish advantages that compound as generative AI becomes the dominant interface for information discovery.

Want more insights like these? Register for Insight Jam, Solutions Review’s enterprise tech community, which enables human conversation on AI. You can gain access for free here!

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SAS’s Jonathan Moran offers commentary on quantum AI in marketing and the next frontier of customer engagement. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Marketing has always been an incubator for innovation, from the Mad Men era of intuitive campaigns to today’s data-driven, AI-enhanced customer engagement strategies.

Technology has been a key driver of change and innovation in marketing. And now yet another emerging technology is poised to redefine how marketers understand, influence, and engage with audiences. Quantum AI, the fusion of quantum computing and artificial intelligence, is still at an early stage but has the potential to fundamentally change marketing.

Evolving From Agentic to Quantum AI

According to a recent study, Marketers and AI: Navigating New Depths, 31 percent of adopters (marketers who are already using agentic AI) expect quantum computing to impact marketing within two years. These early adopters aren’t just dabbling; they’re building the infrastructure to eventually support thousands of autonomous agents that will operate alongside employees, making real-time decisions based on predicted outcomes, optimizing campaigns, and even creating digital environments.

The leap in readiness between adopters of new technology and planners (plan to use the tech in the next year) and adopters (plan to use the tech in the next two years) is striking. While only 16 percent of marketers overall say they understand quantum computing well, that number jumps to 49 percent among agentic AI adopters. These early adopters are not just experimenting with autonomous agents, they’re preparing for quantum’s computational power, its collaboration with AI technologies, and its undoubtedly massive impacts on marketing.

Why Quantum AI Matters for Marketers

Quantum AI combines the probabilistic power of quantum computing with the pattern recognition and decision-making of AI. Unlike classical computers that process bits as 0s or 1s, quantum computers use qubits, which can represent multiple states simultaneously. This makes them ideal for solving complex optimization problems, simulating customer journeys, and analyzing massive datasets in real-time.

For marketers, quantum AI’s vast potential can translate into:

• Faster audience insights: Quantum AI can create and microsegment audiences with more precision and speed than traditional solutions.
• Smarter behavior prediction: Enhanced data analysis leads to better forecasting, personalization, targeting and real-time AI decisioning.
• Real-time optimization: Quantum speeds up A/B and multivariate testing, feedback loops, and pricing models, enabling immediate strategy adjustments.

Unlocking Quantum’s Marketing Potential

Quantum AI is poised to revolutionize many core marketing functions, several being:

Audience Segmentation

Quantum AI enables the processing of vast datasets with more variables and attributes, allowing marketers to refine segments faster and more accurately. This leads to hyper-targeted campaigns with improved performance.

Customer Behavior Prediction

Quantum-enhanced machine learning models can deliver deeper insights into preferences, trends and patterns. This supports more precise personalization and dynamic content delivery based on real-time behavior.

Optimization

Marketing optimization often involves evaluating countless combinations of variables. Quantum AI can dramatically accelerate this process, helping marketers allocate budgets, adjust strategies, appropriately contact to avoid customer saturation, and maximize ROI with unprecedented speed.

Journey simulation

Quantum AI can simulate and help orchestrate complex customer journeys across multiple touchpoints, enabling marketers to anticipate outcomes and tailor experiences proactively.

Real-world Applications Across Industries

The study reveals that quantum AI is already being explored across industries:

• Banking: Advanced predictive analysis for fraud detection, risk mitigation, and customer retention.
• Insurance: Real-time customer journey simulation to improve claims processing and engagement.
• Life Sciences: Hyper-personalization at scale for patient communications and trial recruitment.
• Public Sector: Synthetic data generation and dynamic pricing for citizen programs and services.

Barriers to Adoption: What’s Holding Quantum AI Back? 

Despite its promise, quantum AI faces hurdles. Another SAS survey, conducted in April and involving 500 business leaders globally, found that top concerns related to quantum AI include high cost (38 percent), a lack of understanding or knowledge (35 percent), and uncertainty around real-world applications (31  percent).

These barriers underscore the need for economic conversations around cost, enablement and education, and trusted partnership and ecosystem planning. As quantum evolves, organizations should make quantum education and research more accessible, explore hybrid quantum-classical solutions, and collaborate with companies and broader industry consortia already working on quantum technologies.

A Quantum AI Readiness Checklist for Marketers

If quantum AI still feels like science fiction, here are a few tips to begin your journey:

1. Master traditional, generative, and agentic AI first: Quantum AI builds on the foundations of traditional, generative, and agentic AI. Ensure your team is proficient in these technologies before leaping into quantum.
2. Build quantum into your innovation roadmap: Even if implementation is years away, start planning now. Identify potential use cases, assess data readiness and explore partnerships with quantum leaders.
3. Upskill your team: Invest in training that covers quantum basics, AI ethics and data governance. Encourage cross-functional learning among marketing, IT and data science teams.
4. Start small with hybrid models and projects: Explore hybrid quantum-classical architectures for optimization problems. These models offer a manageable entry point while delivering tangible benefits.
5. Focus on trust and transparency: As with any AI initiative, trust is paramount. Ensure explainability, oversight and ethical use are baked into your quantum AI strategy.

The Quantum Advantage: Speed, Scale, and Strategy

As agentic AI matures and the demand for real-time, hyper-personalized experiences grows, quantum AI’s speed, scale and computing power hold great promise to meet this demand. Marketers who embrace quantum today – by learning more about it and its potential application across marketing functions – will be positioned to lead the next wave of marketing transformation.

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Allie Kelly, CMO at Intentsify, explores AI, its role in intent data, and how it can unlock precision in B2B marketing. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Artificial Intelligence (AI) has had a significant impact across various industries, reshaping strategies and transforming business models. B2B marketing is no exception. Traditionally, B2B marketers have leveraged intent data to identify prospective buyers and create campaigns targeting them. With AI transforming data analysis, uncovering precise patterns and higher-level buyer context, marketers can better leverage intent data to gain a deeper understanding of where buyers and buying groups are in the buying cycle, enabling scalable campaigns and more precise audience targeting.

With the shift from individual buyers to buying groups and the constantly evolving landscape, AI-driven intent data is crucial for ensuring successful go-to-market (GTM) strategies.

The State of Intent Data in B2B Marketing

In the current environment, B2B marketing teams are struggling to maximize ROI on intent data. To effectively leverage intent data and maximize ROI, teams must understand how buyer signals are sourced, scored, and categorized before applying the data to their campaigns. Without this deeper understanding, it can be difficult to reach buyers at the right time and in the most effective way. With intent data being collected from a myriad of sources, these insights often lack transparency and can become siloed, diminishing the potential effectiveness when creating buyer models.

As the buying cycle becomes lengthier and more complex, understanding the when and why in buyer engagement is key. Recent Forrester data highlights that 81 percent of buyers have expressed dissatisfaction with the B2B buying process and their chosen providers, underscoring the need for more precise approaches to intent-driven data strategies. Simultaneously, research also shows that 75 percent of B2B buyers prefer a rep-free sales experience, highlighting the need to maximize value at every touchpoint in the buyer journey.

The Impact of AI on Intent Data on B2B Marketing

Traditional intent data tools rely on static data, such as website visits and form fills, but AI models enable the rapid processing of behavioral signals and context in real-time. By shifting from reactionary insights based on previous data to predictive analytics and recommendations, marketers can take advantage of benefits including:

AI Driven Data Analysis – Maximizing Data Value

AI can enable marketing teams to analyze large stores of intent data, cutting through the noise and providing critical insights and recommendations for marketing and sales teams, allowing for more strategic and targeted buyer engagement.

Target Buyer Groups – Solution-Level Intent Modeling

Rather than leveraging category modeling, AI-powered intent data solutions can utilize solution-level intent modeling to differentiate the weight of each captured behavior, providing deeper insights at the account, buying group, or persona level.

Quality Data – Understanding Customer Intent

In the past, marketing teams have relied on volume-based metrics like clicks to gauge buyer interest. AI-powered solutions can provide key context related to customer intent by recognizing behavioral patterns and providing insights into the consumer’s stage in the buying journey and their level of interest.

How to Leverage AI-Driven Intent Data to Maximize ROI

As the B2B marketing space and the buyer journey continue to evolve, AI-powered intent data is emerging as a powerful tool for maximizing the effectiveness of GTM strategies and empowering marketing teams to engage buyers with precision. To maximize the ROI on AI-powered intent data, the marketing and sales teams must align by integrating insights into CRM tools.

However, before considering integration, here are four key elements marketers should consider when identifying the best vendor for their business:

Signal Fidelity Over Signal Volume

The era of vanity metrics is over. CMOs should demand intent data partners who can demonstrate why a signal matters, not just that it exists. Consider providers who offer granular transparency into signal weighting methodologies and can differentiate between passive content consumption and active problem-solving behavior. The question isn’t how many signals did we capture?—it’s how many signals actually predicted buying behavior? Insist on seeing decay curves, false positive rates, and retrospective conversion analysis.

Integration Architecture as a Competitive Moat

Intent data solutions should function as connective tissue across the entire revenue tech stack—not another data silo. CMOs should evaluate vendors on their ability to operationalize insights in real-time across CRM, MAP, advertising platforms, and sales enablement tools. The most sophisticated CMOs are building “intent orchestration layers” where AI-powered signals automatically trigger coordinated plays across channels. If a vendor can’t explain their API strategy and bi-directional data flows in the first meeting, keep looking.

Buying Group Intelligence, Not Just Account Scoring

Individual account scores are table stakes. The next frontier is understanding the composition, dynamics, and readiness of buying committees. CMOs should seek partners who can map relationship networks within target accounts, identify the emergence of new stakeholders, and detect shifts in buying group consensus. The winning vendors are those who can answer: Which three people need to align for this deal to progress, and what content will bridge their divergent priorities?

Adaptive Learning Systems Over Static Models

The most dangerous assumption is that buyer behavior remains constant. CMOs should prioritize intent data vendors who employ continuous model retraining based on actual conversion outcomes—not industry benchmarks. Partners should demonstrate how their AI adapts to each unique buyer journey, incorporate feedback loops from closed-loop revenue data, and evolve as market conditions shift. Ask the hard question: How does your model perform differently for us versus your other clients, and can you prove it?

Conclusion

As AI continues to evolve, its role in shaping the increasingly complex buyer journey has become more apparent. With traditional intent data becoming less effective, the future of B2B marketing success depends on a combination of human knowledge and AI-powered insights. This will enable marketers to make the most of their intent data through more informed decisions on GTM strategies and buyer engagement, giving them a competitive edge in the market.

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The editors at Solutions Review have compiled the following list to highlight some of the most valuable lead generation services for software vendors to consider incorporating into their marketing strategies.

Software vendors operate in an increasingly saturated marketplace, with more competition for customer attention than ever. With the rapid growth of AI in search, traditional marketing tactics are yielding diminishing returns, prompting many vendors to shift toward sophisticated lead generation strategies that prioritize quality over quantity. The goal should be to identify and pursue prospects who demonstrate genuine purchase intent, instead of casting as wide a net as possible.

To that end, the Solutions Review editors have compiled the following list to spotlight some of the most effective lead generation services that have proven to deliver the results software vendors across various categories need.

The Most Valuable Lead Generation Services for Software Vendors

Account-Based Marketing Platforms

Account-based marketing (ABM) strategies help businesses target clearly defined audiences. Instead of creating content for a customer persona, ABM means targeting marketing materials at real-life prospects and accounts a company has in its database. Selecting these accounts should involve identifying the candidates with the most opportunities for expansion and growth. The benefit of ABM stems from its ability to integrate intent data, technographic information, and behavioral signals to create hyper-personalized engagement sequences that treat individual prospects as unique markets.

With an ABM platform, companies can utilize advanced attribution modeling to track prospect interactions across extended sales cycles, identify buying committee members early in the research phase, nurture each stakeholder with role-specific content and messaging, and analyze historical deal patterns with predictive analytics to surface the accounts most likely to convert within specific timeframes. While account-based marketing can be a tougher sell for vendors with limited resources, it can provide a significant boost to teams that have to manage complex deals while maintaining their pipeline.

Custom Webinars

As widespread as webinars have become since 2020, they remain an underutilized yet highly effective lead generation mechanism for software vendors across verticals. Custom webinars are carefully curated to target individual prospects or specific prospect segments, addressing their unique technical challenges and business objectives with maximum precision.

A webinar’s advantage lies in its ability to transform passive consumers of marketing content into more active and attentive participants. The extended engagement time, live-video demonstration, and opportunity for Q&A allow for deeper technical demonstrations and real-time objection handling that compressed formats cannot accommodate. These features excel at nurturing mid-funnel prospects who have moved beyond initial awareness but require additional validation before committing to formal evaluations. The format naturally qualifies participants through registration requirements and attendance behavior, creating a self-selecting audience of genuinely interested prospects.

However, developing, producing, releasing, and marketing a well-made webinar can require more time and resources than some companies have. That’s where partnerships can help (more on those in a moment), as they enable even smaller vendors to promote their platforms with high-quality, persuasive video content at a fraction of the cost it would take to do so in-house.

Partnership and Channel Programs

Partnership and channel programs are probably the easiest way for smaller software vendors to generate leads. These programs can pay off by helping brands establish a formal referral network that incentivizes partners to identify and develop opportunities within their existing client bases. One perk of partnership programs is their flexibility, as they can range from a simple referral fee arrangement to a comprehensive co-selling initiative with shared revenue models. Successful programs provide partners with sales enablement resources, technical training, and marketing support, creating reciprocal benefits that generate consistent lead flow over extended periods.

Software vendors can even partner with other vendors by evaluating a potential partner’s market presence, client relationships, and solution complementarity. The most effective collaborations involve solutions that naturally integrate or address sequential needs in client technology stacks. Partners with established trust relationships can accelerate prospect conversion through credible third-party recommendations.

Content Syndication Networks

Content syndication is another helpful, relatively inexpensive way to get eyes on your company. By working with a content syndication network, independently or via partnerships, brands can distribute thought leadership content across extensive publisher ecosystems, generating leads through strategic placement and audience targeting. The networks can even utilize sophisticated matching algorithms that connect vendor content with relevant audiences based on demonstrated interests and behavioral patterns to maximize targeting.

By optimizing and adapting content for various publisher environments and audience preferences, software vendors can gain exposure across multiple industry publications, technology blogs, and professional networks without needing to manage individual publisher relationships. The aggregated reach will often exceed what vendors could achieve through direct publisher negotiations, too, making it another versatile strategy for growing brands to consider.

Programmatic Display Advertising

Paid ads are a reliable marketing tool, but programmatic advertising goes further. Modern programmatic platforms use machine learning to identify prospects who exhibit behaviors similar to those of a company’s existing customers. The system will then serve those prospects personalized advertisements across premium publisher networks. Software vendors benefit from the ability to create custom audiences based on technographic data, funding events, hiring patterns, and technology stack changes —signals that generally indicate an organization’s readiness for new software investments.

Programmatic advertising can require a marginally higher budget, especially for brands in niche markets. Still, getting meaningful impressions from a clearly defined customer segment can go a long way toward improving reach, brand identity, and pipeline growth.

Influencer Partnership Programs

Influencer partnerships in the software industry operate differently from consumer-focused campaigns. These programs usually involve collaborations with industry analysts, technical consultants, and respected practitioners, emphasizing technical credibility and thought leadership over broad reach metrics. The trust these voices have in a given market can amplify a software vendor’s messaging.

The structure of these partnerships ranges from formal ambassador programs with ongoing commitments to project-based collaborations focused on specific campaigns or product launches. Successful programs identify influencers whose audiences align precisely with vendor target markets and whose expertise complements the software solution positioning. More advanced programs may involve compensation models that offer anything from equity participation to exclusive access to product roadmaps, co-creation opportunities for thought leadership content, and more. These deeper partnerships can foster authentic advocacy that resonates more effectively with sophisticated software buyers who can distinguish between genuine endorsements and paid promotions.

One of the challenges of influencer partnerships is tracking and measuring attribution. As such, vendors should develop sophisticated tracking mechanisms that capture indirect influence on prospect research and evaluation activities, as the qualitative impact on brand perception and market positioning often may exceed traditionally quantifiable lead generation metrics.

Want to learn more about how online events like solution spotlights, demo days, and expert roundtables can generate leads for your software company? Get in touch with us today!

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Kinsta’s Roger Williams offers commentary on how to get noticed and not disappear in AI tools like ChatGPT and Claude. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Search engine optimization (SEO) was once the baseline online method for companies to build their brand, get noticed and generate traffic. However, today, individuals increasingly use AI applications such as ChatGPT, Claude or Perplexity as search engines instead of a traditional Google search. As a result, organizations need new approaches to ensure they are appearing in these applications. Companies need to build content-matching AI outputs to reach this goal.

The approaches for artificial intelligence optimization (AIO) are not the same as traditional SEO. While traditional search engines remain popular, AI search presents a transformative change. AI better understands natural language and semantics, is contextually and intent aware, and provides users with a conversational pathway to learn more. AI-powered search is better at understanding intent, which is crucial for its ability to provide users with a time-saving productivity tool.

The End of Traditional SEO?

Is traditional SEO disappearing? It will need to evolve, but it won’t become obsolete anytime soon. AI-driven search is altering how users find information, but user experience, authoritative content and technical optimization remain essential for both SEO and AIO. To stay relevant, businesses need long-term visibility in both models, so they need a forward-looking AI-focused content strategy complemented with traditional SEO practices.

While AI assistants like ChatGPT are experiencing rapid growth, reaching 400 million weekly active users as of May 2025, their traffic is still significantly overshadowed by Google Search. Google processes approximately 13.7 billion searches per day in 2025 , maintaining a dominant 90.14% share of the global search engine market. Despite the surge in AI tool usage, traditional search engines like Google continue to be the primary source of online information discovery.

Reworking Content for AI Discovery

AI-generated search looks for clarity and context. It appeals to users because it outputs direct answers and offers relevant questions to continue the conversation. To appeal to this dynamic, companies need content that is easily digestible by large language models. Traditional SEO focuses on backlinks and keywords, while AI search needs authoritative and conversational content that accounts for user intent. Here are some tips:

• Use expansive FAQ sections and concise summaries (consider using AI tools to develop more FAQs) so AI can extract relevant information efficiently. Structured data like FAQPage and HowTo schema make it easier for the AI engines to understand and then output content.

• Review existing content to ensure it offers an authoritative and conversational voice and matches expected AI search outputs. For example, traditional SEO focused on short keyword phrases, like “Best hosting WordPress”. But now people are more likely to search using full questions, such as “Which is the best hosting provider for WordPress?” Test common queries with various AI tools, compare the results to your current FAQ and other pages and adjust if there are disconnects. (Be sure to always keep traditional SEO in mind as Google remains the main driver of traffic for the near future.)

• Focus on the underlying intent of users, not the keywords. For example, if users are searching for “How to choose the best barbeque grill,“ create content with a specific step by step guide, not just broad information.

• Use heading tags such as H2 and H3, which are often used for questions, and make sure the accompanying content clearly answers each one.

• Ensure the content pages load quickly by using fast hosting. If your page takes too long to load, it may not be crawled or fully read. A fast-loading page ensures the AI crawlers can access and interpret your content without interruption.

Strong E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) helps ensure your content is seen as credible and valuable by AI tools like ChatGPT and Claude. These models prioritize information from reliable, well-structured sources when generating answers. By clearly demonstrating first-hand experience, expert knowledge, and a trustworthy digital presence, you increase the chances that your content is cited or surfaced in AI responses, not overlooked.

Making the right changes to optimize content for AI also requires staying abreast of the latest AI search platforms and the quickly changing landscape.

Focusing on the Right AI Players

Businesses should focus on AI search platforms that align with their industry and audience. For example, Google’s AI Overviews and Bing’s AI-powered search are key for broad visibility, while ChatGPT, Perplexity and Claude influence AI-driven discovery. Optimizing for these platforms requires brands to develop structured, conversational and authoritative content. Creating this content enables AI models to then reference and summarize the information in a way that works with the typical AI query output.

Google’s introduction of AI Overviews and the new AI Mode is a clear signal that search is moving toward a more conversational, assistant-like experience. Instead of listing links, AI Mode focuses on providing direct, contextual answers, reshaping how users interact with Google. As this article from iLoveSEO explains, AI Mode is designed to guide users through multi-step queries, suggesting follow-up questions and offering richer responses, making it feel more like a conversation than a search.

Getting Help from Hosting & Marketing Tools

When businesses want to offload technical burdens or lack deep expertise, managed hosting platforms built for WordPress can offer a reliable foundation. While these platforms don’t fix SEO or content strategy directly, they ensure that well-built sites are delivered securely, quickly, and consistently—critical factors for visibility in both traditional search engines and AI-powered tools like ChatGPT and Claude. Traditional SEO depends on metadata, keywords, and backlinks, while AI models favor contextual relevance and structured data. A quality hosting partner helps maintain fast load times, proper content delivery, and performance monitoring—ensuring the work you’ve done to optimize content actually reaches users and models alike.

Traffic from AI tools like ChatGPT tends to convert better than search traffic because users arrive with clearer intent. AI answers often highlight trusted sources directly, so if your brand is featured, visitors are more likely to be engaged and ready to act.

Companies can use AI-powered marketing tools to improve optimization. These tools can:

• Automate keyword research (Be sure to include traditional SEO tools to include search volumes.)

  • Predict search intent behind terms

  • Group keywords by funnel stage (TOFU/MOFU/BOFU)

  • Suggest keyword clusters based on semantic relevance

• Improve content creation

  • Creating content briefs

  • Adjusting the content for readability and improving the paragraph structure and flow

  • Suggest internal links based on context.

• Offer real-time trend analysis

• Perform competitor research

These marketing tools can streamline processes but lack SEO agencies’ critical thinking and expertise. The ideal approach is to blend AI-powered insights with human expertise. This hybrid strategy gives content teams and leadership visibility that expands both traditional and AI search, ensuring content reaches the most eyes and produces optimal results.

Future-Proofing

AI search’s players and processes change rapidly, so companies need to respond with an agile approach. They can test their queries across different models to ensure company content appears in results and make content adjustments if necessary. Business leaders and content teams also need to stay informed about emerging AI models. They can follow research hubs, check for updated news from big developers such as Anthropic or OpenAI, engage with AI search communities and use resources including Search Engine Journal to stay informed of shifts in the SEO landscape. Developing partnerships with SEO and AI specialists can increase this visibility and help companies adapt quickly to new developments.

Companies must adopt a dual-optimization strategy to stay competitive in both AI and traditional search. This means crafting context-rich and conversational content—ideal for AI queries—while also adhering to SEO fundamentals such as keyword targeting, internal linking and structured data. With emerging models such as DeepSeek, which blends retrieval-augmented generation (RAG) with real-time search data, content must go beyond keywords and focus on intent, freshness and semantic depth.

Content teams need to start adapting their content production to AI search. By auditing content for AI readiness and employing a strategy that covers both AI and traditional SEO, companies can best position themselves for the future while maintaining present visibility and authority.

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The editors at Solutions Review have compiled the following list to spotlight some of the best custom webinar service providers for software vendors to consider working with for their virtual event needs.

It’s hard to think back to when virtual events and webinars weren’t a fundamental part of modern marketing. However, they’re a newer addition to the standard toolkit than the tried-and-true classics like social media and email marketing. With the explosion of virtual events, custom webinar services have become essential to any successful marketing strategy, regardless of industry focus or business size.

To help you identify the best webinar service providers and virtual event management companies for your brand, our editors have compiled a list to spotlight some of the industry’s leading providers. The listed service providers have been selected due to the diversity of their features, general acclaim from users, and relevance in today’s market.

The Best Custom Webinar Service Providers for Software Vendors

Solutions Review

Description: Over 10 million IT executives, directors, and decision makers come to the Solutions Review collection of enterprise technology sites every year, and with the addition of Insight Jam, that reach has only grown. There are over one million subscribers between the Solutions Review and Insight Jam YouTube accounts, making it easier than ever to create targeted, custom webinar experiences that connect leading software vendors from multiple industries with their target audience. Solution Review’s experienced, in-house production and audience outreach teams will optimize the content and event marketing to reach the right audiences at the right time.

Demio

Description: Demio is a webinar platform built to help marketers engage and convert their audiences through live conversations. With live, on-demand, and automated webinar offerings, Demio enables teams to curate virtual experiences that build relationships with an audience. Demio’s webinar tools include branded email notifications, engagement analytics, webinar data tracking, integration with leading CRM platforms, custom registration fields, branded features, and more.

GPJ

Description: George P. Johnson (GPJ) is a global strategic experience marketing agency that provides clients with integrated experiential programs that leverage digital, mobile, and physical brand activations. The company’s virtual and hybrid event services can help businesses of all sizes to amplify their message, empower sales teams with actionable analytics, support multiple events across portfolios, and maximize audience reach. GPJ has an in-house production team with experience managing virtual broadcasts, streaming, set design, production, direction, and content creation.

BigMarker

Description: BigMarker is a customizable platform for webinars, virtual, and hybrid events. It helps companies, organizations, and universities host highly-customized live, on-demand, automated presentations, trainings, and events to create and maintain meaningful conversations and relationships with prospects, customers, and other stakeholders. BigMarker’s webinar-specific features include HD screen-sharing, breakout rooms, whiteboarding tools, dial-in telephony, no presenter limits, and support for up to 50,000 attendees.

TSI Consultants

Description: TSI Consultants is a digital marketing firm specializing in services and solutions for companies in the insurance industry. Alongside its inbound sales, inbound marketing, consultation, and HubSpot-specific offerings, TSI Consultants provides several live and on-demand webinar production and management services to help clients stay engaged with their audiences. Those services include webinar hosting, marketing promotion, custom slide designs, moderation, rehearsals, conversation outlines, and more.

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Spencer Bradley, the Vice President of Business Development and Sales at Solutions Review, shares some insights and statistics on how enterprise tech vendors are helping reshape the marketing landscape in 2025 (and beyond).

Enterprise technology vendors in 2025 are navigating a complex marketing environment shaped by AI innovation, economic uncertainty, and increased buyer skepticism. As a result, their marketing budgets are being strategically reallocated toward performance-driven, measurable outcomes. Here’s how they’re spending: 

1) Content That Converts: SEO, Thought Leadership, and Product Education

• Priority Spend: Blogs, whitepapers, product comparison guides, and video explainers.
• Why: B2B buyers are self-educating more than ever before. Content must map directly to intent stages. 
• Format Trends: 
  • AI-generated and human-refined content for scale 
  • Interactive assets (ROI calculators, solution finders) 
  • First-party research reports for media outreach 

2) Account-Based Marketing (ABM) & Personalization 

• Priority Spend: Targeted LinkedIn ads, intent data platforms (e.g., Bombora, G2), and personalized content hubs. 
• Why: Broad-based demand gen is inefficient. ABM targets revenue-qualified accounts, not just MQLs. 
• Emerging Trend: Combining ABM with AI email agents for automated but highly personalized outbound.

3) Influencer and Analyst Engagement

• Priority Spend: Paid analyst relations (Forrester, Gartner alternatives), podcast guest spots, co-branded webinars 
• Why: Buyers trust third-party validation more than vendor content 
• Trend: Rise of “micro-influencers” and niche B2B creators with trusted followings 

4) Performance Marketing with a Hard Pivot to ROI

• Priority Spend: Google Search and Display, LinkedIn Lead Gen, and retargeting.
• Why: Every dollar must prove ROI fast; CMOs are under pressure to tie activity to pipeline.
• Shift: There is a smaller budget for top-of-funnel impressions and more toward bottom-funnel conversion.

5) Event Hybridization and Virtual Briefings

• Priority Spend: Custom virtual events, roundtables, and briefings with strategic accounts.
• Why: Trade show ROI is under scrutiny. Vendors prefer intimate, controlled formats.
• Trend: On-demand replays with gated CTAs for post-event lead capture.

6) AI and Automation-Driven MarTech Investments

• Priority Spend: AI content tools, intent scoring, conversational marketing, and predictive analytics.
• Why: Marketing teams are expected to do more with less. AI unlocks scale. 
• Trend: Consolidating tech stacks around platforms with native AI integration.

7) Community & Owned Media 

• Priority Spend: Branded newsletters, YouTube channels, owned media (like Solutions Review and Insight Jam).
• Why: Trust and audience access are more valuable than traffic spikes.
• Trend: Building media-like properties in-house to control reach and reduce dependency on third-party platforms.

Areas Seeing Reduced Budget: 

• Traditional PR retainers with little attribution.

• High-cost in-person trade shows, unless tied to clear revenue opportunities.

• Overly broad brand campaigns with no measurable impact on the pipeline.

Summary 

In 2025, enterprise tech vendors will be laser-focused on revenue influence, AI-powered efficiency, and content authority. Marketing budgets will be aligned with pipeline velocity, not vanity metrics.

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Chris Brownlee, the SVP of Product at Yext, explains why brands must adapt to Gen Z’s social media search habits to remain relevant and competitive. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Recent data has shown a fundamental change in how Gen Z discovers and evaluates products and services. More than two-thirds of Gen Z consumers now start their search journeys on social media platforms like Instagram and TikTok, bypassing traditional search engines altogether. This behavioral shift signals a broader evolution in digital habits and raises urgent questions for brands about how and where they appear online.

A Generational Shift in Information Search

The trajectory of digital interaction across generations shows a clear shift in how information is sought and processed. Generation X grew up in an era of analog technology, where information was often accessed through physical means like newspapers and directories. Millennials witnessed the advent of digital technology during their formative years, using early computers and mobile phones, which shaped their search behaviors and communication styles.

In contrast, Gen Z represents the first truly digital native generation. Now in their early to late twenties, they have grown up with high-speed internet, social media, and instant access to information. This generation is now entering the market as influential consumers with disposable incomes and purchasing power. Though Generation Alpha (born 2010 to 2024) is still emerging, it is evident that this group will be influenced by advancements in AI and voice technology.

The Rise of Social Media as a Search Tool

The rise of social media platforms as primary search tools for Gen Z is a game-changer for brands. Unlike previous generations, which primarily relied on traditional search engines like Google, Gen Z users increasingly turn to platforms like Instagram and TikTok for product recommendations, service reviews, and brand discovery. This shift highlights the need for brands to cultivate a strong presence on these platforms to capture the attention of this demographic.

Older generations are also adapting to these new search habits. With advancements in technology, users are now accustomed to receiving direct answers from AI assistants like ChatGPT and voice-activated devices rather than sifting through lists of links. This emphasizes the importance for businesses to ensure that all their online information, such as addresses, opening hours, and other factual details, is accurate and up-to-date. Inaccurate information can lead to misinformation and diminish trust, making it critical for companies to maintain control over their digital footprint.

The Multi-Platform Approach

In today’s digital landscape, information is accessible within seconds, setting high expectations for quick and accurate results. Research indicates that 50 percent of Google users, regardless of age, abandon a search result within nine seconds, and only 9 percent venture beyond the first page of results. Additionally, recent data suggests that discoverability is influenced by the breadth of platforms a brand utilizes. Brands that maintain a presence across multiple publishers see increased click-through rates from Google.

Interestingly, around 17 percent of website traffic originates from sources other than Google. This statistic highlights the importance of not relying solely on one platform for data management and visibility. Brands must ensure they are present and consistent across various digital channels to maximize their reach and engagement.

The Impact of Data Consistency

For Gen Z, authenticity and trustworthiness are pivotal factors in their purchasing decisions. They scrutinize brands based on their product offerings and how they present themselves online and engage with customers. Any inconsistencies in a brand’s digital information can lead to negative reviews and damage its reputation. This generation is especially vocal about their experiences on social media and review platforms, which can significantly influence other potential customers.

Furthermore, search algorithms like Google prioritize accuracy and consistency. Discrepancies in information, such as conflicting opening hours across various platforms, can confuse algorithms and negatively impact a brand’s search ranking. Companies that appear lower in search results are less likely to be noticed by users, regardless of age.

Navigating the Digital Landscape

Many businesses find maintaining accurate and consistent information across numerous digital platforms daunting. The complexity increases with the number of locations and information points a company needs to manage. To effectively navigate this challenge, businesses should consider employing a knowledge graph – a comprehensive tool that consolidates all data and information in one place. This allows brands to manage their digital footprint efficiently, ensuring consistency across all touchpoints.

A knowledge graph enables businesses to track and update information in real-time, ensuring that potential customers receive accurate details when they search for products or services. This proactive approach not only helps in reaching Gen Z but also caters to customers across all generations.

The evolving digital landscape requires brands to adapt their strategies to meet the expectations of a new generation of consumers. With Gen Z leading the shift towards social media as a primary search tool and the rise of multi-platform information sources, businesses must prioritize their digital presence. Ensuring data accuracy and consistency across all platforms is crucial for maintaining trust and enhancing discoverability. By leveraging advanced tools like knowledge graphs and embracing a multi-platform approach, brands can effectively engage with today’s digitally savvy consumers and stay ahead in the competitive market.

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